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Increased ranking change in wheat breeding under climate change


The International Maize and Wheat Improvement Center develops and annually distributes elite wheat lines to public and private breeders worldwide. Trials have been created in multiple sites over many years to assess the lines’ performance for use in breeding and release as varieties, and to provide iterative feedback on refining breeding strategies1. The collaborator test sites are experiencing climate change, with new implications for how wheat genotypes are bred and selected2. Using a standard quantitative genetic model to analyse four International Maize and Wheat Improvement Center global spring wheat trial datasets, we examine how genotype–environment interactions have changed over recent decades. Notably, crossover interactions—a critical indicator of changes in the ranking of cultivar performance in different environments—have increased over time. Climatic factors explained over 70% of the year-to-year variability in crossover interactions for yield. Yield responses of all lines in trial environments from 1980 to 2018 revealed that climate change has increased the ranking change in breeding targeted to favourable environments by ~15%, while it has maintained or reduced the ranking change in breeding targeted to heat and drought stress by up to 13%. Genetic improvement has generally increased crossover interactions, particularly for wheat targeted to high-yielding environments. However, the latest wheat germplasm developed under heat stress was better adapted and more stable, partly offsetting the increase in ranking changes under the warmer climate.

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Fig. 1: Map of the site distribution of four spring wheat trials in the IWIN.
Fig. 2: Changes in crossover occurrence for the four spring wheat trials.
Fig. 3: Values of crossover occurrence estimated from real yield versus climate-fitted yield variations for the four spring wheat trials and prediction accuracy of the model.
Fig. 4: Estimated changes in crossover caused by climate change and breeding progress for the four wheat trials.

Data availability

The original IWIN nursery data are publicly available at The climate data are from the European Centre for Medium-Range Weather Forecast ( The cleaned nursery data and corresponding climate information prepared for this study are available at

Code availability

The data analysis scripts, including trend analysis, regression and plotting, were developed with Python v.3.7.3 and were deposited in GitHub ( Requests for analysis scripts for the analyses performed can be directed to W.X.


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This work was supported by projects funded by the National Natural Science Foundation of China (grant nos. 4147104 and 41171093) and the project granted by the Foundation for Food and Agriculture Research. This study was also supported by the CGIAR research programme on wheat agri-food systems (CRP WHEAT) and the CGIAR Platform for Big Data in Agriculture. We thank T. Lin, D. Wang, T. Guo and others from the HAU for discussions on the methods and results of this study, and Z. He from the Chinese Academy of Agricultural Sciences for assistance in obtaining the data.

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Authors and Affiliations



M.P.R., W.X. and J.C. conceived the study. T.P., U.S., K.S., C.M. and N.A. collected and processed the data. W.X. analysed the data. W.X. and M.P.R. wrote the paper, and all authors contributed to the writing.

Corresponding author

Correspondence to Wei Xiong.

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The authors declare no competing interests.

Additional information

Peer review information Nature Plants thanks Marta Da Silva, J. W. White and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Temporal changes in yield variance.

Yield variance caused by germplasm (a) and environment (b). Yield variances (points) were estimated by the ANOVA method and converted to the precent relative to the regression values for the first year. The lines are linear regression fits estimated by the least squares method, with shading representing the respective 95% confidence intervals.

Extended Data Fig. 2 Changes in the six climatic variables averaged across testing sites in three stages of the growing season.

Veg. vegetative; rep. reproductive; gfi. grain filling. The lines are linear regression fits.

Extended Data Fig. 3 Changes in the variation of the six climatic variables among the sites.

Veg. vegetative; rep. reproductive; gfi. grain filling. The lines are linear regression fits.

Extended Data Fig. 4 Simulation sensitivity to model’s parameters.

Response of simulated yield and crossover occurrence to L1 penalty coefficient of the Lasso regression.

Extended Data Fig. 5 Possibility of crossover occurrence across environment.

Each bin of the histgram represents the ocurrence possibility of crossover in specific site mean yields.

Extended Data Fig. 6 Result sensitivity to the choosing of yield variation – climate relationship models.

Results only show the trends of crossover occurrence caused by climate change.

Extended Data Fig. 7 Reported wheat yield and changing trends for each of the four nurseries.

The lines are linear regression fits estimated by the least squares method, with shading representing the respective 95% confidence intervals. Points are reported mean yield for each line and year.

Supplementary information

Supplementary Information

Supplementary Methods, Figs. 1–4, Tables 1–4 and References.

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Xiong, W., Reynolds, M.P., Crossa, J. et al. Increased ranking change in wheat breeding under climate change. Nat. Plants 7, 1207–1212 (2021).

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